Abstract
ABSTRACTDespite the importance of starch for tree growth, methodological challenges in starch analysis slow down the research on its ecological importance. In this study, a rapid monitoring method was developed for measuring starch content in Pinus taeda L. seedlings after cold treatments. A linear mixed-effects model was used to analyze the effects of cold treatments, seedling tissue types and their interaction on starch content. Mid-infrared (MIR) and near-infrared (NIR) spectra were surveyed, and the results were analyzed using partial least squares regression to determine the starch content. The determination coefficient for calibration and residual predictive deviation were compared between MIR and NIR models to assess the variability of the established models. The results showed that the effects of cold treatments, seedling tissue types and their interaction on starch content were significant. Compared to MIR spectra, NIR spectra is more suitable to estimate starch content in the seedlings. Using NIR spectra, roots provided the most accurate estimates of starch content. The presented guidelines regarding data accuracy as a function of MIR/NIR spectra of samples represent an important methodological reference for starch quantification, which will improve the understanding of the fundamental role of starch in seedlings against environmental forces.
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